Morphogen gradients are widely used to provide cells with the positional information needed to create spatial patterns of gene expression. Such pattern formation underlies a great deal of developmental morphogenesis, and is notable for its accuracy and reproducibility. Indeed, a large fraction of human birth defects are the direct result of relatively small disruptions in pattern formation. In most cases, the formation of morphogen gradients, and the responses of cells to them, is subject to complex regulation by networks of interacting transcription factors, receptors, and co-receptors. It is likely that such regulation evolved to make spatial patterning robust to biologically relevant perturbations (genetic variability, environmental uncertainty, intrinsic stochasticity, etc.). Focusing on the BMP gradient that patterns the antero-posterior axis of the Drosophila wing imaginal disc, we will explore and elucidate the mechanistic basis for robust patterning, through a collaborative approach that closely intertwines experimental biology with mathematical modeling and analysis. Three related areas of investigation will be pursued: First, we will quantify the cell-to-cell variability that normally impedes the ability of tissues to generate sharp borders of gene expression in response to shallow morphogen gradients, and investigate how such spatial noise changes at different stages within the gene regulatory network that controls wing vein patterning. Second, we will pursue recent evidence suggesting that patterning is sensitive not only to levels of morphogen in a gradient but to the local gradient slope as well. As part of this work we will test the hypothesis that slope detection is mediated by the Fat signaling pathway, and serves the purpose of reducing spatial noise. Third, we will extend previous mathematical models of morphogen gradient formation and interpretation in order to elucidate the tradeoffs that arise among regulatory mechanisms that serve as strategies for achieving robustness with respect to individual types of perturbations. In particular, such models will incorporate mechanisms and performance objectives that have not heretofore been analyzed mathematically. Broadly, the goal of this work is to provide a more coherent understanding of how complex regulation of spatially dynamic biological systems is utilized to achieve robust performance under a wide variety of conditions. Ultimately, the results should provide insights into the pathological processes that lead to structural birth defects and other developmental abnormalities. )